// RUN: tf-mlir-translate -mlir-to-graphdef %s -o - | FileCheck %s

func.func @main() {
  tf_executor.graph {
    tf_executor.island wraps "tf.NoOp"() {} : () -> () loc("noop")
    tf_executor.fetch
  }
  func.return
}

// CHECK: node {
// CHECK-NEXT:  name: "noop"
// CHECK-NEXT:  op: "NoOp"
// CHECK-NEXT:  experimental_debug_info {
// CHECK-NEXT:  }

// RUN: not tf-mlir-translate -mlir-to-graphdef %s -o - 2>&1 | FileCheck %s

// CHECK: entry function `main` must be present

func.func @const() {
  tf_executor.graph {
    %0:2 = tf_executor.island wraps "tf.Const"() {device = "TPU:0", name = "const", dtype = "tfdtype$DT_INT32", value = dense<[1, 2]> : tensor<2xi32>} : () -> tensor<2xi32>
    tf_executor.fetch
  }
  func.return

func (e encodable) DeepCopyObject() Object {
	out := e
	out.obj = e.obj.DeepCopyObject()
	copy(out.versions, e.versions)
	return out
}

// NewEncodable creates an object that will be encoded with the provided codec on demand.
// Provided as a convenience for test cases dealing with internal objects.
func NewEncodable(e Encoder, obj Object, versions ...schema.GroupVersion) Object {
	if _, ok := obj.(*Unknown); ok {
		return obj
	}

    attribute, that attribute will be overwritten with the `tf_device.launch`
    device.

    For example:

    ```mlir
      %island:5 = tf_executor.island {
        %a = "tf.opA"() : () -> tensor<i1>
        %launch:2 = "tf_device.launch"() ( {
          %b = "tf.opB"() : () -> tensor<i32>
          %c = "tf.opC"() : () -> tensor<f32>

Sie können mehrere Pfad-Parameter und Query-Parameter gleichzeitig deklarieren, **FastAPI** weiß, was welches ist.

Und Sie müssen sie auch nicht in einer spezifischen Reihenfolge deklarieren.

Parameter werden anhand ihres Namens erkannt:

=== "Python 3.10+"

    ```Python hl_lines="6  8"
    {!> ../../../docs_src/query_params/tutorial004_py310.py!}
    ```

=== "Python 3.8+"

    ```Python hl_lines="8  10"

     *
     * <p>The closures will be “evaluated” on demand whenever the value is needed (e.g. at generation time)</p>
     *
     * @param toSign A closure that produces a File for the object to sign (non File return values will be used as the path to the file)
     * @param classifier A closure that produces the classifier to assign to the signature artifact on demand

// These resources cannot be looked up on-demand.
//
// In some cases, we do not know the IP address of a Service. These services cannot be used for matching
// outbound traffic, as we only have L4 attributes to route based on. However,
// they can be used for Gateways.
// In this case, the key format will be "network/hostname".
// These resources cannot be looked up on-demand.
message Address {
  oneof type {

    absl::Span<Predicate* const> operands, bool is_and) {
  Predicate::Kind pred_kind =
      is_and ? Predicate::Kind::kAnd : Predicate::Kind::kOr;

  IncrementStackDepth stack_frame(this);
  if (stack_frame.HasOverflowed()) {
    return MakeInternedAndOr(
        std::vector<Predicate*>(operands.begin(), operands.end()), pred_kind);
  }

  Predicate::Kind other_pred_kind =
      is_and ? Predicate::Kind::kOr : Predicate::Kind::kAnd;

/**
 * A utility similar to {@link IteratorTester} for testing a {@link ListIterator} against a known
 * good reference implementation. As with {@code IteratorTester}, a concrete subclass must provide
 * target iterators on demand. It also requires three additional constructor parameters: {@code
 * elementsToInsert}, the elements to be passed to {@code set()} and {@code add()} calls; {@code

func.func @dont_inline_tpu_partitioned_call(%arg0: tensor<2xi32> {tf._user_specified_name = "inputs_0"}, %arg1: tensor<2xi32> {tf._user_specified_name = "inputs_1"}) -> tensor<2xi32> {
  %graph = tf_executor.graph {
    %result:2 = tf_executor.island wraps "tf.TPUPartitionedCall"(%arg0, %arg1) {config = "", config_proto = "", executor_type = "", f = @simple_callee} : (tensor<2xi32>, tensor<2xi32>) -> tensor<2xi32>
    tf_executor.fetch %result#0 : tensor<2xi32>
  }